Summary

This presents the renewed analysis of Cryptococcus neoformans start codon usage and context. This uses the best-transcript annotation and corresponding start codon position and sequence map made by Corinne Maufrais in June 2018.

It covers both JEC21 and H99 data. First several analyses on JEC21, then the same analyses on H99, then a joint analysis of signals conserved across both strains.

We check consensus sequences for both “narrow” (NNNNNATG) and “wide” (NNNNNNNNNATGNNN) neighbourhoods of the start codon, and find essentially the same results with both, comparing annotated aATGs to downstream dATGs. Then for the following analyses we use the narrow score.

Load Packages

JEC21 first

Expression: RNA abundance and ribosome-protected-fragments

Load expression data

## # A tibble: 6,634 x 4
## # Groups:   Gene [6,634]
##    Gene        RNA    RPF    TE
##    <chr>     <dbl>  <dbl> <dbl>
##  1 CNM01300  3981. 18260. 4.59 
##  2 CNM01080  8422.  8957. 1.06 
##  3 CNA07570  5811.  7134. 1.23 
##  4 CNG04360  3171.  7048. 2.22 
##  5 CNB02360  3764.  6958. 1.85 
##  6 CNA06350 15019.  6591. 0.439
##  7 CNC00700  2344.  6224. 2.65 
##  8 CNF03840 11321.  6175. 0.545
##  9 CNF02150 15611.  6144. 0.394
## 10 CNF03160  5094.  6114. 1.20 
## # ... with 6,624 more rows

We also calculated hiTrans_JEC21, the top 5% (330) translated genes by RPF TPM.

Ribosome occupancy mostly tracks RNA abundance

ATG Context

Load context data

## # A tibble: 6,636 x 19
##    Gene  aATG.context aATG.pos d1.context d1.posTSS d1.posATG d1.frame
##    <chr> <chr>           <int> <chr>          <int>     <int>    <int>
##  1 CNA0… GACCCCCTTGT…       93 ATAGCTGGT…       226       133        1
##  2 CNA0… ATATTGCCTGA…      102 GTCCACCTT…       163        61        1
##  3 CNA0… GAACTATCAAG…      214 GAGGCTCCG…       512       298        1
##  4 CNA0… ATTTTCAACAG…       81 AGCAATATA…       307       226        1
##  5 CNA0… ACCGTGCACAC…       76 GTATTCGGG…       106        30        0
##  6 CNA0… AATCATACCAA…      117 GCCCCTATC…       186        69        0
##  7 CNA0… CCGACTATAAA…       52 AACCGTGCT…       112        60        0
##  8 CNA0… CTTTCTCTTCA…       77 TGCTATAGC…        98        21        0
##  9 CNA0… TAATCACACAA…      330 CTCATCATC…       391        61        1
## 10 CNA0… AAAAAAAACGC…      146 ACTTGTCGA…       184        38        2
## # ... with 6,626 more rows, and 12 more variables: d2.context <chr>,
## #   d2.posTSS <int>, d2.posATG <int>, d2.frame <int>, u1.context <chr>,
## #   u1.posTSS <int>, u1.posATG <int>, u1.frame <int>, u2.context <chr>,
## #   u2.posTSS <int>, u2.posATG <int>, u2.frame <int>

Annotated ATGs have a Kozak consensus sequence

Highly translated Annotated ATGs have a Kozak consensus sequence

That’s for hiTrans_JEC21, the top 5% (330) translated genes by RPF TPM.

Cytoplasmic Ribosome Annotated ATGs have a Kozak consensus sequence

Upstream ATGs don’t have a consensus

First upstream ATG.

Downstream ATGs don’t have a consensus

First downstream ATG

Downstream ATGs in frame and highly translated don’t have a consensus

Except for 3rd-codon-position bias.

Calculate Information content and scores of consensus motif

Calculate a wide and a narrow consensus sequence

Calculate motif score against the position weight matrix (pwm) for both narrow (-5 from ATG through to ATG) and wide (-9 from ATG to +3) kozak consensus motif. These motifs are taken from the top 5% highly translated genes.

Estimate the information content

Using the sequence logo, details on https://en.wikipedia.org/wiki/Sequence_logo

## # A tibble: 6 x 4
##   Genes    ATG   Width  Infon
##   <chr>    <chr> <chr>  <dbl>
## 1 All      aATG  narrow 1.12 
## 2 HiTrans  aATG  narrow 3.00 
## 3 CytoRibo aATG  narrow 4.20 
## 4 All      d1ATG narrow 0.157
## 5 HiTrans  d1ATG narrow 0.344
## 6 CytoRibo d1ATG narrow 0.537

Information content in bits of highly-translated consensus (excluding 6 bits from ATG), narrow is 3, of wide is 4.7.

Calculate scores of aATG, dATG, uATG against Kozak consensus

We calculate scores using Biostrings::PWMscoreStartingAt.

The best description I could find of this method is: https://support.bioconductor.org/p/61520/

It is just the sum of the matrix product of the PWM with the sequence.

Write scores to file scores_kozak_JEC21.txt.

## # A tibble: 6,636 x 13
##    Gene  aATG.scorekn d1.scorekn d2.scorekn u1.scorekn aATG.scorekw
##    <chr>        <dbl>      <dbl>      <dbl>      <dbl>        <dbl>
##  1 CNA0…        0.736      0.723      0.904      0.968        0.662
##  2 CNA0…        0.813      0.934      0.738      0.868        0.734
##  3 CNA0…        0.877      0.697      0.787      0.803        0.876
##  4 CNA0…        0.889      0.804      0.800     NA            0.865
##  5 CNA0…        0.955      0.709      0.846     NA            0.838
##  6 CNA0…        1.000      0.889      0.701     NA            0.935
##  7 CNA0…        0.977      0.811      0.730     NA            0.893
##  8 CNA0…        0.790      0.922      0.768      0.734        0.798
##  9 CNA0…        0.933      0.923      0.801     NA            0.890
## 10 CNA0…        0.848      0.780      0.790     NA            0.874
## # ... with 6,626 more rows, and 7 more variables: d1.scorekw <dbl>,
## #   d2.scorekw <dbl>, u1.scorekw <dbl>, d1vsan <dbl>, u1vsan <dbl>,
## #   d1vsaw <dbl>, u1vsaw <dbl>

Plot against narrow consensus (-5 to ATG)

Narrow consensus comparing d1ATG to d2ATG score

Narrow consensus d1ATG score by frame

Same again, segregated by expression (enoughRNA, top 50%)

Plot against wide consensus (-9 to +3 from ATG)

Wide consensus comparing d1ATG to d2ATG score

Compare aATG and dATG context by gene

Most dATG scores are less than aATG scores

Most u1ATG scores are less than aATG scores

For highly translated genes, most dATG scores are much less than aATG

Red: high dATG vs aATG Kozak score. Blue: highly translated. Purple: both.

Small negative correlation between dATG and aATG score

Rnarrow = -0.065; Rwide = -0.049

There may be some trend but it is weak

Boxplots show enough_RNA only.

Genes with unusual dATG vs aATG narrow score

Those genes are in this list:

## # A tibble: 330 x 3
##    Gene     aATG.scorekn d1.scorekn
##    <chr>           <dbl>      <dbl>
##  1 CNI00340        0.675      0.967
##  2 CNK00900        0.709      0.999
##  3 CNA01530        0.677      0.956
##  4 CNI00670        0.711      0.990
##  5 CNI00690        0.677      0.948
##  6 CNB01880        0.626      0.893
##  7 CNK02980        0.702      0.968
##  8 CNL05790        0.643      0.908
##  9 CNA07070        0.646      0.911
## 10 CNB00790        0.735      0.999
## # ... with 320 more rows

dATG vs aATG ribosome occupancy depends on the context

For top 3315 / 50% of genes by mean RNA TPM.

dATG vs aATG ribosome occupancy depends on the context, geometric mean across reps

There is slight enrichment in high-score dATGs in frame near the aATG

Compare score difference to localization predictions

Load predictions from mitofates

In input file JEC21_mitofates_26June2018.txt.

Genes with high dATG vs aATG score are enriched in mitochondrial presequences

However, mito-localized genes do not have a distinctive aATG context

It’s just a subset: the dual-localized ones.

Negative correlation of dATG and aATG score only for mitochondrial localization signal

Mitofates and d1ATG/d2ATG narrow score

uATGs inhibit translation of the main ORF

uATGs are associated with lower absolute translation

uATGs are associated with lower translation efficiency

uATGs associated with lower translation efficiency are over 20nt from TSS

uATG score does not strongly affect TE

We suspect that uATG is associated with lower TE if the uATG has * position at least 20nt downstream from TSS * higher score

This figure shows that, for genes with only 1 uATG, this correlation is weak.

List of genes with low TE and uATGs far from TSS

Check these for ribosome occupancy at uATG.

## # A tibble: 666 x 8
## # Groups:   Gene [?]
##    Gene       RNA   RPF       TE uATGCt uATGCtmin20 u1.cxtn  u2.cxtn 
##    <chr>    <dbl> <dbl>    <dbl>  <int>       <int> <chr>    <chr>   
##  1 CNH02505 579.  0.498 0.000859     12          12 CCGGAATG GACTAATG
##  2 CND00170  98.6 0.175 0.00177       5           5 TGGCCATG GCCATATG
##  3 CNJ03050  58.2 0.109 0.00187       9           8 ACTCGATG TTCTGATG
##  4 CNC06230 279.  0.574 0.00205       4           4 ATCGAATG TCGGAATG
##  5 CNA05520 246.  0.562 0.00228       5           5 CAGCTATG ATCGAATG
##  6 CNE00345  70.1 0.251 0.00358       3           3 CATCGATG GCCCTATG
##  7 CNL03915  72.1 0.277 0.00384       6           6 ACCAGATG GGAAGATG
##  8 CNI00340  25.0 0.119 0.00475       7           6 TGTGCATG ACAATATG
##  9 CNF00250  34.4 0.165 0.00478      38          38 TCGGAATG GAATGATG
## 10 CND00880  25.1 0.141 0.00563       2           1 TCAAGATG CCATTATG
## # ... with 656 more rows
## # A tibble: 7 x 8
## # Groups:   Gene [?]
##   Gene       RNA   RPF     TE uATGCt uATGCtmin20 u1.cxtn  u2.cxtn 
##   <chr>    <dbl> <dbl>  <dbl>  <int>       <int> <chr>    <chr>   
## 1 CNA07610  50.0  5.85 0.117       1           1 TCCGTATG <NA>    
## 2 CNF00330 182.   3.20 0.0176      8           8 AAAAAATG CAAAAATG
## 3 CNG00290  58.3  4.38 0.0751      1           1 GCAGGATG <NA>    
## 4 CNG04240 123.   5.50 0.0446      0           0 <NA>     <NA>    
## 5 CNH02210  42.4  1.68 0.0396      1           1 CCACAATG <NA>    
## 6 CNL04930 203.  17.3  0.0853      2           2 CGACAATG ACTTTATG
## 7 CNM02470 171.  17.0  0.0993      2           2 CCAGAATG CCATCATG

uATG vs aATG ribosome occupancy depends on the context

For top 3315 / 50% of genes by mean RNA TPM.

Back to table of contents

H99 second

Expression: RNA abundance and ribosome-protected-fragments

Load expression data

## # A tibble: 6,790 x 4
## # Groups:   Gene [6,790]
##    Gene          RNA    RPF    TE
##    <chr>       <dbl>  <dbl> <dbl>
##  1 CNAG_06125 10270. 20140. 1.96 
##  2 CNAG_06101  8775.  8494. 0.968
##  3 CNAG_05762  7529.  7499. 0.996
##  4 CNAG_00779  3896.  7432. 1.91 
##  5 CNAG_03127  6254.  7164. 1.15 
##  6 CNAG_06222  6631.  6772. 1.02 
##  7 CNAG_04011 13306.  6772. 0.509
##  8 CNAG_01455 12670.  6548. 0.517
##  9 CNAG_05525  6970.  6461. 0.927
## 10 CNAG_03739  6515.  6383. 0.980
## # ... with 6,780 more rows

We also calculated hiTrans_H99, the top 5% (330) translated genes by RPF TPM.

Ribosome occupancy mostly tracks RNA abundance

ATG Context

Load context data

## # A tibble: 6,791 x 19
##    Gene  aATG.context aATG.pos d1.context d1.posTSS d1.posATG d1.frame
##    <chr> <chr>           <int> <chr>          <int>     <int>    <int>
##  1 CNAG… TACTTACGCGA…       70 AAATTCACT…       100        30        0
##  2 CNAG… GAACTTCGATC…       52 TCTCCCGCC…       114        62        2
##  3 CNAG… TGTCTCCTTGA…      104 ACTTACGCC…       189        85        1
##  4 CNAG… CACATACGTAA…      214 CCGAACGGC…       256        42        0
##  5 CNAG… GACTATACAAA…       55 GGAGGTGGG…       163       108        0
##  6 CNAG… AACCATACAAA…       99 CAAAGCCAT…       259       160        1
##  7 CNAG… ACCGTGCACAC…       75 GTATTCGGA…       105        30        0
##  8 CNAG… GTTTTCAACAG…       73 CCCATCAGA…       380       307        1
##  9 CNAG… GTACTATTGAA…      206 GAGGCTCCG…       513       307        1
## 10 CNAG… TACAAGCTTGA…       90 GGCCGCCTT…       151        61        1
## # ... with 6,781 more rows, and 12 more variables: d2.context <chr>,
## #   d2.posTSS <int>, d2.posATG <int>, d2.frame <int>, u1.context <chr>,
## #   u1.posTSS <int>, u1.posATG <int>, u1.frame <int>, u2.context <chr>,
## #   u2.posTSS <int>, u2.posATG <int>, u2.frame <int>

Annotated ATGs have a Kozak consensus sequence

Highly translated Annotated ATGs have a Kozak consensus sequence

That’s for hiTrans_H99, the top 5% (330) translated genes by RPF TPM.

Cytoplasmic Ribosome Annotated ATGs have a consensus sequence

Ideally would fix this more nicely.

Upstream ATGs don’t have a consensus

First upstream ATG.

Downstream ATGs don’t have a consensus

First downstream ATG

Downstream ATGs in frame and highly translated don’t have a consensus

Except for 3rd-codon-position bias.

Calculate Information content and scores of consensus motif

Calculate a wide and a narrow consensus sequence

Calculate motif score against the position weight matrix (pwm) for both narrow (-5 from ATG through to ATG) and wide (-9 from ATG to +3) kozak consensus motif. These motifs are taken from the top 5% highly translated genes.

Estimate the information content

Using the sequence logo details on https://en.wikipedia.org/wiki/Sequence_logo

## # A tibble: 6 x 4
##   Genes    ATG   Width  Infon
##   <chr>    <chr> <chr>  <dbl>
## 1 All      aATG  narrow 1.02 
## 2 HiTrans  aATG  narrow 3.12 
## 3 CytoRibo aATG  narrow 4.38 
## 4 All      d1ATG narrow 0.139
## 5 HiTrans  d1ATG narrow 0.265
## 6 CytoRibo d1ATG narrow 0.501

Information content in bits of highly-translated consensus (excluding 6 bits from ATG), narrow is 3.12, of wide is 4.87.

Calculate scores of aATG, dATG, uATG against Kozak consensus

Write scores to file scores_kozak_H99.txt.

## # A tibble: 6,791 x 11
##    Gene  aATG.scorekn d1.scorekn u1.scorekn aATG.scorekw d1.scorekw
##    <chr>        <dbl>      <dbl>      <dbl>        <dbl>      <dbl>
##  1 CNAG…        0.866      0.786      0.854        0.834      0.689
##  2 CNAG…        0.834      0.848     NA            0.793      0.808
##  3 CNAG…        0.795      0.855     NA            0.741      0.781
##  4 CNAG…        0.881      0.752      0.855        0.893      0.661
##  5 CNAG…        0.979      0.727     NA            0.874      0.625
##  6 CNAG…        0.979      0.710     NA            0.917      0.663
##  7 CNAG…        0.960      0.819     NA            0.849      0.798
##  8 CNAG…        0.876      0.799     NA            0.864      0.782
##  9 CNAG…        0.896      0.796      0.822        0.850      0.758
## 10 CNAG…        0.879      0.938      0.691        0.743      0.863
## # ... with 6,781 more rows, and 5 more variables: u1.scorekw <dbl>,
## #   d1vsan <dbl>, u1vsan <dbl>, d1vsaw <dbl>, u1vsaw <dbl>

Plot against narrow consensus (-5 to ATG)

Plot against wide consensus (-9 to +3 from ATG)

Compare aATG and dATG context by gene

Most dATG scores are less than aATG scores

Most u1ATG scores are less than aATG scores

For highly translated genes, most dATG scores are much less than aATG

Small negative correlation between dATG and aATG score

Rnarrow = -0.044; Rwide = -0.031

There may be some trend but it is weak

Boxplots show enough_RNA only.

Genes with unusual dATG / aATG narrow score

Those genes are in this list:

## # A tibble: 330 x 3
##    Gene       aATG.scorekn d1.scorekn
##    <chr>             <dbl>      <dbl>
##  1 CNAG_07473        0.606      0.970
##  2 CNAG_04147        0.644      0.984
##  3 CNAG_04764        0.642      0.948
##  4 CNAG_07801        0.676      0.979
##  5 CNAG_02259        0.676      0.979
##  6 CNAG_03953        0.692      0.991
##  7 CNAG_06278        0.699      0.991
##  8 CNAG_07776        0.702      0.993
##  9 CNAG_04179        0.709      0.991
## 10 CNAG_00165        0.676      0.957
## # ... with 320 more rows

dATG vs aATG ribosome occupancy depends on the context

For top 3315 / 50% of genes by mean RNA TPM.

dATG vs aATG ribosome occupancy and score, geometric mean across reps

Compare score difference to localization predictions

Load predictions from mitofates

In input file H99_mitofates_26June2018.txt.

Genes with high dATG vs aATG score are enriched in mitochondrial presequences

However, mito-localized genes do not have a distinctive aATG context

It’s just a subset: the dual-localized ones.

uATGs inhibit translation of the main aORF

uATGs are associated with lower absolute translation

uATGs are associated with lower translation efficiency

uATGs associated with lower translation efficiency are over 20nt from TSS

List of genes with low TE and uATGs far from TSS

Many of these (CNAG_03140, CNAG_07695, CNAG_06246) are strongly translationally repressed and have good context at the uATG.

## # A tibble: 594 x 8
## # Groups:   Gene [?]
##    Gene         RNA   RPF      TE uATGCt uATGCtmin20 u1.cxtn  u2.cxtn 
##    <chr>      <dbl> <dbl>   <dbl>  <int>       <int> <chr>    <chr>   
##  1 CNAG_03387  38.4 0.223 0.00581     16          16 CGGCCATG TGACAATG
##  2 CNAG_00013  25.8 0.198 0.00768      9           9 GCCCGATG TATTGATG
##  3 CNAG_06757  27.1 0.230 0.00850     30          30 TTTACATG TCCTGATG
##  4 CNAG_03140 187.  2.00  0.0107       6           6 GGAAAATG GACAAATG
##  5 CNAG_01781  26.7 0.385 0.0144       4           4 TACATATG TGGTAATG
##  6 CNAG_00789  26.8 0.388 0.0145       8           8 TTCGAATG TCATAATG
##  7 CNAG_05319  35.4 0.602 0.0170       2           2 GTGACATG TGATTATG
##  8 CNAG_07866  41.8 0.740 0.0177       3           3 CAACAATG CACCGATG
##  9 CNAG_07695 164.  5.69  0.0346      16          16 CAAAAATG CAAGAATG
## 10 CNAG_07450  44.8 1.55  0.0346       3           3 GTCGAATG GATCGATG
## # ... with 584 more rows
## # A tibble: 5 x 8
## # Groups:   Gene [?]
##   Gene         RNA   RPF     TE uATGCt uATGCtmin20 u1.cxtn  u2.cxtn 
##   <chr>      <dbl> <dbl>  <dbl>  <int>       <int> <chr>    <chr>   
## 1 CNAG_00784  52.8  6.69 0.127       1           0 TCCGTATG <NA>    
## 2 CNAG_03578  43.5  6.20 0.143       1           1 GCAGGATG <NA>    
## 3 CNAG_05574  30.0  2.66 0.0885      1           1 CCACAATG <NA>    
## 4 CNAG_06246 196.  24.6  0.125       2           2 CCAGAATG CCATCATG
## 5 CNAG_07813 148.  20.1  0.136       1           1 CGGCAATG <NA>

uATG vs aATG ribosome occupancy depends on the context

For top 3315 / 50% of genes by mean RNA TPM.

Back to table of contents

Results on conserved genes in H99 and JEC21.

Load list of paralogs

From 2016 Paper.

## # A tibble: 6,341 x 2
##    H99        JEC21   
##    <chr>      <chr>   
##  1 CNAG_01397 CND05080
##  2 CNAG_07825 CNH03545
##  3 CNAG_05539 CNH01890
##  4 CNAG_03635 CNB01365
##  5 CNAG_06621 CNF03970
##  6 CNAG_00830 CNA08090
##  7 CNAG_07556 CNK01100
##  8 CNAG_06796 CNB00060
##  9 CNAG_06009 CNM00180
## 10 CNAG_03522 CNG00710
## # ... with 6,331 more rows

Conservation of gene expression

RNA Abundance

Ribosome Occupancy

Translation efficiency, no threshold

Translation efficiency, filtered by top 50% of expression

Genes with high translation

## # A tibble: 20 x 8
##    H99        JEC21    RNA.H99 RPF.H99 TE.H99 RNA.JEC21 RPF.JEC21 TE.JEC21
##    <chr>      <chr>      <dbl>   <dbl>  <dbl>     <dbl>     <dbl>    <dbl>
##  1 CNAG_06125 CNM01300  10270.  20140.  1.96      3981.    18260.    4.59 
##  2 CNAG_06101 CNM01080   8775.   8494.  0.968     8422.     8957.    1.06 
##  3 CNAG_00779 CNA07570   3896.   7432.  1.91      5811.     7134.    1.23 
##  4 CNAG_03127 CNG04360   6254.   7164.  1.15      3171.     7048.    2.22 
##  5 CNAG_05762 CNF02150   7529.   7499.  0.996    15611.     6144.    0.394
##  6 CNAG_03739 CNB02360   6515.   6383.  0.980     3764.     6958.    1.85 
##  7 CNAG_06222 CNM02240   6631.   6772.  1.02      4689.     6013.    1.28 
##  8 CNAG_00655 CNA06350  12483.   6041.  0.484    15019.     6591.    0.439
##  9 CNAG_04011 CNB04930  13306.   6772.  0.509    19847.     5650.    0.285
## 10 CNAG_06633 CNF03840   9267.   6136.  0.662    11321.     6175.    0.545
## 11 CNAG_01332 CND04480   5923.   6076.  1.03      4638.     5976.    1.29 
## 12 CNAG_03015 CNC00700   4856.   5691.  1.17      2344.     6224.    2.65 
## 13 CNAG_04448 CNI01090   6654.   5950.  0.894     5345.     5891.    1.10 
## 14 CNAG_00640 CNA06200   7701.   5784.  0.751     4883.     6037.    1.24 
## 15 CNAG_00771 CNA07490   6955.   5860.  0.843     7040.     5959.    0.846
## 16 CNAG_04883 CNJ03110   4270.   5872.  1.38      5747.     5908.    1.03 
## 17 CNAG_04726 CNJ01560   8038.   6353.  0.790     6386.     5396.    0.845
## 18 CNAG_00672 CNA06500   9191.   6054.  0.659    14270.     5645.    0.396
## 19 CNAG_05525 CNH01770   6970.   6461.  0.927     4051.     5204.    1.28 
## 20 CNAG_03780 CNB02750   6868.   5685.  0.828     5180.     5878.    1.13

Genes with high TE

## # A tibble: 20 x 8
##    H99        JEC21    RNA.H99 RPF.H99 TE.H99 RNA.JEC21 RPF.JEC21 TE.JEC21
##    <chr>      <chr>      <dbl>   <dbl>  <dbl>     <dbl>     <dbl>    <dbl>
##  1 CNAG_01130 CND02530    47.7   301.    6.30      32.7      297.     9.09
##  2 CNAG_01890 CNK02310   248.   1445.    5.84     279.      1901.     6.82
##  3 CNAG_06150 CNM01520   607.   3347.    5.51     539.      2928.     5.43
##  4 CNAG_02994 CNC06020    68.5   261.    3.81      31.1      218.     7.01
##  5 CNAG_01750 CNC02520   256.   1307.    5.10     311.      1655.     5.33
##  6 CNAG_01727 CNC02320   736.   3570.    4.85     706.      3747.     5.31
##  7 CNAG_01744 CNC02470   104.    315.    3.04      33.5      237.     7.07
##  8 CNAG_04327 CNI02220    44.7   201.    4.51      35.2      197.     5.58
##  9 CNAG_01117 CND02420   436.   2095.    4.81     439.      2275.     5.18
## 10 CNAG_05907 CNF00650    94.5   298.    3.15      66.3      450.     6.79
## 11 CNAG_04640 CNJ00800   217.    822.    3.80     159.       969.     6.10
## 12 CNAG_04313 CNI02360   204.    225.    1.10      30.8      254.     8.23
## 13 CNAG_07373 CNA06000    65.7   302.    4.59      77.8      354.     4.55
## 14 CNAG_05602 CNH02450   381.    675.    1.77      27.4      192.     7.00
## 15 CNAG_06840 CND06220  1196.   2878.    2.41     460.      2895.     6.29
## 16 CNAG_00136 CNA01230    46.4   197.    4.24      45.3      199.     4.40
## 17 CNAG_05884 CNF00890    79.8   294.    3.69      72.8      360.     4.95
## 18 CNAG_06208 CNM02070   251.    976.    3.89     231.       986.     4.27
## 19 CNAG_00992 CND01200   254.    889.    3.50     262.      1168.     4.47
## 20 CNAG_04659 CNJ00950    25.8    55.6   2.16      26.3      152.     5.80

Genes with low TE

To-do: Check which of these have uATGs.

## # A tibble: 20 x 8
##    H99        JEC21    RNA.H99 RPF.H99 TE.H99 RNA.JEC21 RPF.JEC21 TE.JEC21
##    <chr>      <chr>      <dbl>   <dbl>  <dbl>     <dbl>     <dbl>    <dbl>
##  1 CNAG_07695 CNF00330   164.    5.69  0.0346     182.      3.20   0.0176 
##  2 CNAG_03140 CNG04240   187.    2.00  0.0107     123.      5.50   0.0446 
##  3 CNAG_05574 CNH02210    30.0   2.66  0.0885      42.4     1.68   0.0396 
##  4 CNAG_04855 CNJ02770    30.4   2.67  0.0879      87.9     6.69   0.0761 
##  5 CNAG_06614 CNF04050    41.8   4.39  0.105       57.8     4.57   0.0791 
##  6 CNAG_02323 CNE02240    39.1   3.59  0.0918      49.9     5.12   0.103  
##  7 CNAG_03578 CNG00290    43.5   6.20  0.143       58.3     4.38   0.0751 
##  8 CNAG_07813 CNL04930   148.   20.1   0.136      203.     17.3    0.0853 
##  9 CNAG_06246 CNM02470   196.   24.6   0.125      171.     17.0    0.0993 
## 10 CNAG_05319 CNH03140    35.4   0.602 0.0170      35.4     7.69   0.218  
## 11 CNAG_00784 CNA07610    52.8   6.69  0.127       50.0     5.85   0.117  
## 12 CNAG_08027 CNH02090    25.6   2.75  0.107       89.8    13.6    0.152  
## 13 CNAG_02433 CNE01240    38.9   6.78  0.174      114.     11.1    0.0970 
## 14 CNAG_00529 CNA05110    40.2   7.86  0.196       96.7     7.83   0.0809 
## 15 CNAG_05237 CNL03915    33.1   9.02  0.273       72.1     0.277  0.00384
## 16 CNAG_05288 CNH03430    56.4   9.02  0.160       70.0     8.83   0.126  
## 17 CNAG_02867 CNC04820    54.5   7.14  0.131       47.6     7.51   0.158  
## 18 CNAG_01624 CNC01375    34.5   5.07  0.147       30.4     4.36   0.143  
## 19 CNAG_05567 CNH02150    36.1   6.85  0.189       75.9     7.98   0.105  
## 20 CNAG_06782 CNB00170    53.7   8.43  0.157       59.8     9.21   0.154

Genes with dATG score high relative to aATG score

We take transcripts where the overall gene expression (RNA abundance in top 50%), the difference in score (dATG > aATG in top 5%), and the dATG frame are all conserved between H99 and JEC21.

dATG in frame with ATG

Saved to file dvsaATG_highdiffn_inframe_cc.txt.

## # A tibble: 46 x 6
##    H99        JEC21    a.skn.H99 d.skn.H99 a.skn.JEC21 d.skn.JEC21
##    <chr>      <chr>        <dbl>     <dbl>       <dbl>       <dbl>
##  1 CNAG_07473 CNB01880     0.606     0.970       0.626       0.893
##  2 CNAG_07776 CNI00670     0.702     0.993       0.711       0.990
##  3 CNAG_00165 CNA01530     0.676     0.957       0.677       0.956
##  4 CNAG_02545 CNE00210     0.667     0.941       0.683       0.943
##  5 CNAG_07801 CNL06190     0.676     0.979       0.686       0.904
##  6 CNAG_01544 CNC06400     0.727     0.979       0.722       0.978
##  7 CNAG_03953 CNB04410     0.692     0.991       0.743       0.947
##  8 CNAG_04179 CNI03160     0.709     0.991       0.727       0.947
##  9 CNAG_02431 CNE01260     0.727     1           0.730       0.943
## 10 CNAG_05722 CNF02520     0.669     0.913       0.677       0.918
## 11 CNAG_06353 CNN00820     0.659     0.904       0.669       0.904
## 12 CNAG_02880 CNC04930     0.667     0.913       0.683       0.911
## 13 CNAG_03996 CNB04810     0.667     0.899       0.626       0.847
## 14 CNAG_00517 CNA04990     0.733     0.934       0.703       0.942
## 15 CNAG_02259 CNE02870     0.676     0.979       0.697       0.835
## 16 CNAG_03396 CNG01890     0.634     0.862       0.643       0.854
## 17 CNAG_00086 CNA00760     0.737     0.957       0.744       0.956
## 18 CNAG_07873 CNH00360     0.733     0.948       0.751       0.946
## 19 CNAG_04219 CNI03610     0.780     0.979       0.771       0.978
## 20 CNAG_04604 CNJ00430     0.790     0.991       0.799       0.989
## # ... with 26 more rows

dATG out of frame

Saved to file dvsaATG_highdiffn_outframe_cc.txt.

## # A tibble: 12 x 6
##    H99        JEC21    a.skn.H99 d.skn.H99 a.skn.JEC21 d.skn.JEC21
##    <chr>      <chr>        <dbl>     <dbl>       <dbl>       <dbl>
##  1 CNAG_06278 CNN00160     0.699     0.991       0.726       0.989
##  2 CNAG_04054 CNB05380     0.718     0.979       0.725       0.978
##  3 CNAG_02894 CNC05065     0.784     0.979       0.800       0.978
##  4 CNAG_06006 CNM00150     0.716     0.934       0.783       0.933
##  5 CNAG_02809 CNC04270     0.802     0.979       0.791       0.978
##  6 CNAG_03008 CNC06190     0.827     1           0.817       1    
##  7 CNAG_03370 CNG02120     0.804     0.970       0.795       0.968
##  8 CNAG_01667 CNC01780     0.842     0.991       0.836       0.989
##  9 CNAG_00784 CNA07610     0.693     0.842       0.695       0.836
## 10 CNAG_02578 CNK00690     0.826     0.946       0.793       0.943
## 11 CNAG_01241 CND03590     0.758     0.883       0.760       0.899
## 12 CNAG_07780 CNI00090     0.876     1           0.867       1
  • CNN00160 two-component-like sensor kinase TCO7
  • CNB05380 SUI1/eIF1, translation initiation factor.
  • CNC06190 has a domain conserved with eIF2.
  • CNG02120 freqenin calcium-binding protein, FRQ1 homolog
  • CNC05065/CNM00150/CNC04270/CNC06190/CNA07610/CND03900, all hypothetical or uncharacterized.
  • CNC01780 prenyltransferase, COQ2 homolog
  • CNK00690 regulation of meiosis-related, PCH2 homolog
  • CND03590 protein phosphatase I nuclear regulatory subunit, SDS22 homolog
  • CNI00090 farnesyltranstransferase, BTS1 homolog
  • CND03900 has a W2 eIF4-gamma/eIF5/eIF2-epsilon - like domain
  • CNB03280 mRNA transcription modulator, CCR4-NOT ubiquitin ligase subunit MOT2 homolog.

Genes with very different aATG scores, JEC21 vs H99

Filtered for enough RNA (top 50%)

## # A tibble: 132 x 6
##    H99        JEC21    a.skn.H99 d.skn.H99 a.skn.JEC21 d.skn.JEC21
##    <chr>      <chr>        <dbl>     <dbl>       <dbl>       <dbl>
##  1 CNAG_04147 CNI02850     0.644     0.984       0.946       0.890
##  2 CNAG_06000 CNM00090     0.686     0.915       0.965       0.756
##  3 CNAG_03486 CNG01060     0.704     0.967       0.965       0.778
##  4 CNAG_06196 CNM01950     0.733     0.634       0.989       0.745
##  5 CNAG_00690 CNA06680     0.717     0.857       0.955       0.653
##  6 CNAG_06446 CNN01710     0.765     1           1           0.629
##  7 CNAG_01092 CND02180     0.689     0.925       0.922       0.877
##  8 CNAG_01188 CND03130     0.651     0.793       0.882       0.734
##  9 CNAG_07437 CND03020     0.725     0.960       0.955       0.883
## 10 CNAG_05678 CNF02960     0.689     0.910       0.917       0.932
## # ... with 122 more rows
## # A tibble: 132 x 6
##    H99        JEC21    a.skn.H99 d.skn.H99 a.skn.JEC21 d.skn.JEC21
##    <chr>      <chr>        <dbl>     <dbl>       <dbl>       <dbl>
##  1 CNAG_04488 CNI00690     0.947     0.970       0.677       0.948
##  2 CNAG_03968 CNB04570     0.991     0.767       0.741       0.989
##  3 CNAG_00529 CNA05110     0.948     0.811       0.701       0.886
##  4 CNAG_03410 CNG01740     0.991     0.742       0.753       0.989
##  5 CNAG_04751 CNJ01820     0.960     0.860       0.736       0.922
##  6 CNAG_04089 CNB05680     0.967     0.817       0.744       0.890
##  7 CNAG_04899 CNJ03230     0.948     0.784       0.736       0.968
##  8 CNAG_05720 CNF02540     0.957     0.757       0.756       0.956
##  9 CNAG_02703 CNK01910     0.895     0.747       0.698       0.883
## 10 CNAG_03638 CNB01390     0.909     0.793       0.717       0.667
## # ... with 122 more rows

Many of these have the expected structure where homologs differ only at the N-terminus. There appears to be a swap between a near-ATG start codon, and a poor-context ATG, between the species.

Higher aATG score in JEC21:

  • CNAG_04147/CNI02850, putative RNA helicase RRP3 homolog
  • CNAG_06000/CNM00090, putative glycoprotein
  • CNAG_03486/CNG01060, peptidylprolyl isomerase, Scer has CPR2/CPR5 homolgs localized to ER and vacuole.
  • CNAG_06353/CNN00820, tRNA pseudouridine synthase, exciting, CNAG_06353 has weak mito loc seq, Scer has PUS1/PUS2 nuc/mito homologs.
  • CNAG_06446/CNN01710, mitochondrial splicing suppressor Mss51 homolog, clear mito preseq in both.
  • CNAG_01092/CND02180, hypothetical protein conserved in crypto, mito preseq in both
  • CNAG_01188/CND03130, ATGATG start in H99, not interesting
  • CNAG_05678/CNF02960, putative transmembrane protein involved in ammonia production.
  • CNAG_07645/CNE03115 Autophagy protein Atg12, H99 CNAG_07645 has non-homologous annotated N-terminal extension that seems improbably.

Higher aATG score in H99:

  • CNAG_00529/CNA05110, sulfite transporter, Scer Ssu1 homolog.
  • CNAG_03410/CNG01740, upstream ATG in JEC21 close to TSS, prob not real
  • CNAG_04089/CNB05680, again upstream ATG in JEC21 close to TSS, prob not real. Note that CNAG_03410/CNAG_04089 are paralogs
  • CNAG_04751/CNJ01820, hypothetical protein conserved in tremellomycetes.
  • CNAG_02703/CNK01910, hypothetical protein conserved in tremellomycetes.
  • CNAG_03638/CNB01390, identical ATG context in fungidb, check.
  • CNAG_05504/CNH01580, Iah1, pattern unclear, JEC21 just has poor start codons.
  • CNAG_05692/CNF02820, sphinganine kinase, Scer YSR3/LCB2 homolog.
  • CNAG_07609/CNC03180, putative RNA helicase, weak start context in JEC21 predicts less protein in cell. Or most translation initiation is from much later ATG? DDX51/DBP6 homolog.

These look like mostly misannotated in one strain, or not interesting. Is the upstream start codon in one strain actually used? Check for ribosome footprints and for other features (homology, mito localization seq). It would be nice to have an additional filter here.

GO analysis of conserved genelists - OUT OF DATE Oct 2018

This was done on 25th June, with values generated by CryptoATGcontext then. Not a reproducible analysis here!

I performed GO analysis with PANTHER.db on JEC21 gene names. PANTHER version 13.1 Released 2018-02-03, Overrepresentation test on GOslim terms.

Link: http://www.pantherdb.org/tools/compareToRefList.jsp

dATG score high compared aATG and dATG out of frame, 14 genes

File dvsaATG_highdiffn_outframe_cc.txt.

No significant GO terms.

dATG score high compared aATG and dATG in frame, 44 genes

File dvsaATG_highdiffn_inframe_cc.txt.

Enriched in Biological processes:

  • tRNA aminoacylation for protein translation< translation < protein metabolic process < primary metabolic process < metabolic process
  • tRNA metabolic process < RNA metabolic process < nucleobase-containing compound metabolic process
  • Unclassified

Molecular Function:

  • aminoacyl-tRNA ligase activity < ligase activity < catalytic activity

Cellular Component:

  • cytosol < cytoplasm < intracellular < cell part

Highly translated, 291 genes

File hiTrans_cc.txt.

Enriched BPs include:

  • oxidative phosphorylation
  • glycolysis
  • translation
  • protein folding
  • cation transport
  • mitochondrial transport

Enriched MFs include:

  • transmembrane transporter activity
  • structural constituent of ribosome
  • translation elongation factor activity

Enriched CCs include:

  • proton-transporting ATP synthase complex
  • ribosome
  • mitochondrial inner membrane

High translation efficiency, 174 genes

File hiTE_cc.txt.

Enriched BPs include:

  • pentose-phosphate shunt < monosaccharide metabolic process
  • tRNA aminoacylation for protein translation < translation
  • acyl-CoA metabolic process
  • tricarboxylic acid cycle
  • cellular amino acid biosynthetic process
  • nuclear transport

Enriched MFs include:

  • translation elongation factor activity
  • translation initiation factor activity
  • aminoacyl-tRNA ligase activity

Enriched CCs include:

  • cytosol < cytoplasm

Low translation efficiency, 284 genes

File loTE_cc.txt.

Enriched BPs include:

  • anion transport

Enriched MFs, no sig. results.

Enriched CCs, no sig. results.

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